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检索条件"任意字段=Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing"

共 1494 条记录，以下是231-240 订阅

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Multi-slice Tomographic Reconstruction: To Couple or Not to Couple 16

Multi-slice Tomographic Reconstruction: To Couple or Not to ...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Gopal, Preeti Chandran, Sharat Svalbe, Imants Rajwade, Ajit Indian Inst Technol Dept Comp ViGIL Bombay Maharashtra India IITB Monash Res Acad Bombay Maharashtra India Monash Univ Dept Phys & Astron Clayton Vic Australia

ISBN: (纸本)9781450347532

Recent work in tomography focuses on algorithms that enable faster and more accurate reconstruction from as few measurements as possible. We review the advantage of jointly reconstructing multiple slices and show that joint reconstruction may suffer in the presence of adjacent dissimilar slices. This gives rise to the need to detect similarity or dissimilarity of unknown images before performing joint reconstruction. We propose a method to detect 'similar' slices directly from their tomographic measurements and juxtapose these similar slices. Since the images themselves are not available by definition, we compute similarity between slices based on image moments;these in turn are estimated in a novel way from Radon projection moments. A segmented least squares algorithm is then designed to couple only similar slices. Our results confirm the benefit of this method for tomographic reconstruction.

关键词： Compressive sensing Radon projection moments image moments tomographic reconstruction

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Document image Segmentation as a Spectral Partitioning Problem

Document Image Segmentation as a Spectral Partitioning Probl...

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6th indian conference on computer vision, graphics and image processing

作者： Dasigi, Praveen Jain, Raman Jawahar, C. V. IIIT Hyderabad Ctr Visual Informat Technol Hyderabad 500032 Andhra Pradesh India

ISBN: (纸本)9781424442195

State of art document segmentation algorithms employ adhoc solutions which use some document properties and iteratively segment the document image. These solutions need to be adapted frequently and sometimes fail to perform well for complex scripts. This calls for a generalized solution that achieves a one shot segmentation that is globally optimal. This paper describes one such solution based on the optimization problem of spectral partitioning which makes the decision of proper segmentation based on the Spectral properties of the pairwise similarity matrix. The solution described in the paper is shown to be general, global and closed form. The claims have been demonstrated on 142 page images from a Telugu book, in a script set in both poetry and prose layouts. This particular class of scripts has been proved to be challenging for the existing state of the art algorithms, where the proposed solution achieves significant results.

关键词： Iterative methods

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A Fast Method for Very Low Bit-Rate Coding of Hyperspectral images 11

A Fast Method for Very Low Bit-Rate Coding of Hyperspectral ...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Kumar, Saurabh Chaudhuri, Subhasis Indian Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9781450366151

In this work, we propose a computationally efficient compressive sensing based approach for very low bit rate lossy coding of hyperspectral (HS) image data by exploiting the redundancy inherent in this imaging modality. We divide the HS datacube into subsets of adjacent bands, each of which is encoded into a coded snapshot using a random code matrix. These coded snapshot images are encoded using the wavelet-based SPIHT compression technique. The decompression from the coded snapshots at the receiver is done using the orthogonal matching pursuit with the help of an overcomplete dictionary learned on a general purpose training dataset. We provide ample experimental results and performance comparisons to substantiate the usefulness of the proposed method. In the proposed technique the encoder is free from any decoder and it offers a significant saving in computation and yet yields a much higher compression quality.

关键词： image coding

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A Bottom-Up and Top-Down Approach for image Captioning using Transformer 11

A Bottom-Up and Top-Down Approach for Image Captioning using...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Parameswaran, Sandeep Narayan Das, Sukhendu Indian Inst Technol Madras Chennai Tamil Nadu India

ISBN: (纸本)9781450366151

Describing the contents of an image automatically has been a fundamental problem in the field of artificial intelligence and computer vision. Existing approaches are either top-down, which start from a simple representation of an image and convert it into a textual description;or bottom-up, which come up with attributes describing numerous aspects of an image to form the caption or a combination of both. Recurrent neural networks (RNN) enhanced by Long Short-Term Memory networks (LSTM) have become a dominant component of several frameworks designed for solving the image captioning task. Despite their ability to reduce the vanishing gradient problem, and capture dependencies, they are inherently sequential across time. In this work, we propose two novel approaches, a top-down and a bottom-up approach independently, which dispenses the recurrence entirely by incorporating the use of a Transformer, a network architecture for generating sequences relying entirely on the mechanism of attention. Adaptive positional encodings for the spatial locations in an image and a new regularization cost during training is introduced. The ability of our model to focus on salient regions in the image automatically is demonstrated visually. Experimental evaluation of the proposed architecture on the MS-COCO dataset is performed to exhibit the superiority of our method.

关键词： Long short-term memory

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Automatic Video Matting through Scribble Propagation 16

Automatic Video Matting through Scribble Propagation

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Sonane, Bhoomika Ramakrishnan, Sainandan Raman, Shanmuganathan IIT Gandhinagar Ahmadabad Gujarat India VJTI Bombay Maharashtra India

ISBN: (纸本)9781450347532

Video matting is an extension of image matting and is used to extract the foreground matte from an arbitrary background of every frame in a video sequence. An automatic scribbling approach based on the relative motion of the foreground object with respect to the background in a video is introduced for video matting. The proposed scribble propagation and the subsequent isolation of foreground and background is much more intuitive than the conventional trimap propagation approach used for video matting. Alpha maps are propagated according to the optical flow estimated from the consecutive frames to get a preliminary estimate of the foreground and background in the following frame. Accurate scribbles are placed near the boundary of the foreground region for re fining the scribbled image with the help of morphological operations. We show that a high quality matte of foreground object can be obtained using a state-of-the-art image matting technique. We show that the results obtained using the proposed method are accurate and comparable with that of other state-of-the-art video matting techniques.

关键词： Video matting Optical flow Computational photography

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A Framework for Analysis of \Surveillance Videos

A Framework for Analysis of \Surveillance Videos

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6th indian conference on computer vision, graphics and image processing

作者： Choudhary, Ayesha Chaudhury, Santanu Banerjee, Subhashis Indian Inst Technol Dept Comp Sci & Engn Delhi India Indian Inst Technol Dept Elect Engn Delhi India

ISBN: (纸本)9781424442195

In this paper, we propose a novel framework for automated analysis of surveillance videos. By analysis, we imply summarizing and mining of the information in the video for learning usual patterns and discovering unusual ones. We approach this video analysis problem by acknowledging that a video contains information at multiple levels and in multiple attributes. Each such component and co-occurrences of these component values play an important role in characterizing an event as usual or unusual. Therefore, we cluster the video data at multiple levels of abstraction and in multiple attributes and view these clusters as a summary of the information in the video. We apply cluster algebra to mine this summary from multiple perspectives and to adapt association learning for automated selection of components because of which the event is unusual. We also propose a novel incremental clustering algorithm.

关键词： Clustering algorithms

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Fast Line Segment Matching Based on Point-Line Affine Invariants and Pairwise Constraints 14

Fast Line Segment Matching Based on Point-Line Affine Invari...

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14th International conference on graphics and image processing (ICGIP)

作者： Zhang, Haowei Zhang, Zhihe Yan, Zhaoyan Li, Yan Lanzhou Univ Lanzhou Peoples R China

ISBN: (纸本)9781510666313;9781510666320

In this paper we propose a method about line segment matching based on point-line invariants. We use ORB and EDlines, two efficient and stable methods, to extract point and line features respectively. Then we introduce the implementation details of our matching algorithm. It includes affine invariance of the ratio of the distance from two coplanar points to the line and pairwise constraint of the geometric relationship between two lines. In order to eliminate mismatches, we use a series of methods to optimize the result of our algorithm. We set up a scoring mechanism for candidate matches and the final matches will be given by evaluating the voting matrix. Its performance is evaluated by extensive experiments. The results show that our proposed method outperforms the mainstream methods, and are robust to rotation, scale, blur and other transformations.

关键词： computer vision image feature extraction feature tracking line matching

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Local ternary patterns and maximum bipartite matching for face recognition

Local ternary patterns and maximum bipartite matching for fa...

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2011 3rd National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2011

作者： Patnaik, Renuka Gupta, Raj Mittal, Anurag Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai 600036 India

ISBN: (纸本)9780769545998

Local Binary Pattern (LBP) has been the successful feature descriptor used for face recognition. The basic idea in this method is to convert from an intensity space to an order space where the order of neighboring pixels is used to create a monotonic change illumination-invariant code for each point in the image. A drawback for this method, however, is that, in homogenous regions, the order of the pixel with respect to its neighbors is quite noisy. In this paper, we propose to use a third value which indicates if two pixels are similar in value. We also propose to match these patterns using maximum bipartite matching rather than histogram matching. Significant performance boost was found when compared to LBP and other standard methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). We present our results on Extended Yale and FERET datasets. © 2011 IEEE.

关键词： Local binary pattern

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Informed Multimodal Latent Subspace Learning via Supervised Matrix Factorization 16

Informed Multimodal Latent Subspace Learning via Supervised ...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Gaurav, Ramashish Verma, Mridula Shukla, K. K. BHU Indian Inst Technol Varanasi Uttar Pradesh India

ISBN: (纸本)9781450347532

Matrix factorization technique has been widely used as a popular method to learn a joint latent-compact subspace, when multiple views or modals of objects (belonging to single-domain or multiple-domain) are available. Our work confronts the problem of learning an informative latent subspace by imparting supervision to matrix factorization for fusing multiple modals of objects, where we devise simpler supervised additive updates instead of multiplicative updates, thus scalable to large scale datasets. To increase the classification accuracy we integrate the label information of images with the process of learning a semantically enhanced subspace. We perform extensive experiments on two publicly available standard image datasets of NUS WIDE and compare the results with state-of-the-art subspace learning and fusion techniques to evaluate the efficacy of our framework. Improvement obtained in the classification accuracy confirms the effectiveness of our approach. In essence, we propose a novel method for supervised data fusion thus leading to supervised subspace learning.

关键词： image Classification Joint Subspace Learning Supervised Matrix Factorization

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Ghosting Free HDR for Dynamic Scenes via Shift-maps 16

Ghosting Free HDR for Dynamic Scenes via Shift-maps

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Prabhakar, K. Ram Babu, R. Venkatesh Indian Inst Sci Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (纸本)9781450347532

Given a set of sequential exposures, High Dynamic Range imaging is a popular method for obtaining high-quality images for fairly static scenes. However, this typically suffers from ghosting artifacts for scenes with significant motion. Also, existing techniques cannot handle heavily saturated regions in the sequence. In this paper, we propose an approach that handles both the issues mentioned above. We achieve robustness to motion (both object and camera) and saturation via an energy minimization formulation with spatio-temporal constraints. The proposed approach leverages information from the neighborhood of heavily saturated regions to correct such regions. The experimental results demonstrate the superiority of our method over state-of-the-art techniques for a variety of challenging dynamic scenes.

关键词： Exposure Fusion High Dynamic Range imaging

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